Gangliosides protect bowel in an infant model of necrotizing enterocolitis by suppressing pro‐inflammatory signals of infection and hypoxia
Bibliographic record
Abstract
Necrotizing enterocolitis (NEC) is an inflammatory bowel disease of neonates with high morbidity in premature infants. Hypoxia‐ischemia, infection and enteral feeding are risk factors while feeding human milk is protective. The role of vasoactive and inflammatory mediators in NEC remains elusive due to limitations in models. An infant bowel model of NEC was developed to test the hypothesis that gangliosides, human milk glycolipids with anti‐inflammatory properties, modulate the inflammatory response of infant bowel to infection and hypoxia. Viable, non‐inflamed infant bowel was obtained from 0–3 month infants requiring bowel surgery. Cultured infant bowel was treated with E. coli lipopolysaccharide (LPS) and hypoxia in the presence and absence of gangliosides. Bowel necrosis and production of nitric oxide, endothelin‐1, eicosanoids, hydrogen peroxide and pro‐inflammatory cytokines were measured. Gangliosides reduced bowel necrosis in response to E. coli LPS. Gangliosides also suppressed bowel production of nitric oxide, endothelin‐1, LTB 4 , PGE 2 , H 2 O 2, IL‐1β, IL‐6 and IL‐8 in response to E. coli LPS and hypoxia. These findings indicate a bowel protective effect of gangliosides through pro‐inflammatory signal suppression, modulation of vasoactive mediators and anti‐oxidant effects. The results provide a strong rationale for ganglioside use in food products to treat inflammatory bowel diseases. This research project was funded by the Canadian Institute of Health and the Natural Sciences and Engineering Research Council.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".